






"""simple docstring"""


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						def is_in_circle(a__     ,      a__ ) -> bool:
												lowerCAmelCase   :Optional[Any]						=			sqrt((x**2) + (y**2) )
												# Our circle has a radius of 1, so a distance
												# greater than 1 would land outside the circle.
												return distance_from_centre <= 1

						# The proportion of guesses that landed in the circle
						lowerCAmelCase   :Optional[int]						=			mean(
						    int(is_in_circle(uniform(-1.0     ,      1.0 )     ,      uniform(-1.0     ,      1.0 ) ) )
						    for _ in range(a__ ) )
						# The ratio of the area for circle to square is pi/4.
						lowerCAmelCase   :str						=			proportion * 4
						print(F"""The estimated value of pi is {pi_estimate}""" )
						print(F"""The numpy value of pi is {pi}""" )
						print(F"""The total error is {abs(pi - pi_estimate )}""" )


def        UpperCAmelCase      (					a__     ,      a__     ,      a__ = 0.0     ,      a__ = 1.0     ,      ):





						'''simple docstring'''






						return mean(
						    function_to_integrate(uniform(a__     ,      a__ ) ) for _ in range(a__ ) ) * (max_value - min_value)


def        UpperCAmelCase      (					a__     ,      a__ = 0.0     ,      a__ = 1.0 ):





						'''simple docstring'''






						def identity_function(a__ ) -> float:
												return x

						lowerCAmelCase   :Tuple						=			area_under_curve_estimator(
						    a__     ,      a__     ,      a__     ,      a__ )
						lowerCAmelCase   :int						=			(max_value * max_value - min_value * min_value) / 2

						print('******************' )
						print(F"""Estimating area under y=x where x varies from {min_value} to {max_value}""" )
						print(F"""Estimated value is {estimated_value}""" )
						print(F"""Expected value is {expected_value}""" )
						print(F"""Total error is {abs(estimated_value - expected_value )}""" )
						print('******************' )


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						def function_to_integrate(a__ ) -> float:
												return sqrt(4.0 - x * x )

						lowerCAmelCase   :Dict						=			area_under_curve_estimator(
						    a__     ,      a__     ,      0.0     ,      2.0 )

						print('******************' )
						print('Estimating pi using area_under_curve_estimator' )
						print(F"""Estimated value is {estimated_value}""" )
						print(F"""Expected value is {pi}""" )
						print(F"""Total error is {abs(estimated_value - pi )}""" )
						print('******************' )


if __name__ == "__main__":
		import doctest

		doctest.testmod()







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__SCREAMING_SNAKE_CASE   =   {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE   =   [
		    'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'AdaptiveEmbedding',
		    'TransfoXLForSequenceClassification',
		    'TransfoXLLMHeadModel',
		    'TransfoXLModel',
		    'TransfoXLPreTrainedModel',
		    'load_tf_weights_in_transfo_xl',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE   =   [
		    'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFAdaptiveEmbedding',
		    'TFTransfoXLForSequenceClassification',
		    'TFTransfoXLLMHeadModel',
		    'TFTransfoXLMainLayer',
		    'TFTransfoXLModel',
		    'TFTransfoXLPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
		from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_transfo_xl import (
				    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    AdaptiveEmbedding,
				    TransfoXLForSequenceClassification,
				    TransfoXLLMHeadModel,
				    TransfoXLModel,
				    TransfoXLPreTrainedModel,
				    load_tf_weights_in_transfo_xl,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_transfo_xl import (
				    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFAdaptiveEmbedding,
				    TFTransfoXLForSequenceClassification,
				    TFTransfoXLLMHeadModel,
				    TFTransfoXLMainLayer,
				    TFTransfoXLModel,
				    TFTransfoXLPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE   =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)